What is Predictive Analytics and How Can it Give You an Unfair Advantage?

Data gives us information of what has previously worked or not. We all have 20/20 eyesight in retrospect. But what if you had 20/20 vision—not in the rearview mirror, but in the moment?

Predictive analytics’ lofty objective is that. Let’s examine some actual instances of businesses utilizing or preparing to utilize predictive analytics.

What if Amazon shipped your goods before you ordered them?

Amazon applied for a patent in 2012 for a system that would allow them to prepare and send your things before you even placed an order, using predictive analytics.

Based on your behavior, their predictive analytics engine would determine whether to start preparing the item based on how likely it was that you would buy it. Amazon would be able to shorten delivery times as a result, strengthening their position as the swiftest online store.

What if you paid less insurance on days you drove safest?

What is Predictive Analytics and How Can it Give You an Unfair Advantage
What is Predictive Analytics and How Can it Give You an Unfair Advantage

This morning, as I was seeking a car insurance quote, I came across a new option.

For insurance companies, employing statistical analysis and prediction models to reduce risk is nothing new, but using real-time data to modify insurance premiums seems novel to me.

The flip side of this is that emergency services might be immediately alerted to potentially dangerous drivers, allowing them to respond more quickly, increasing road safety and lowering crime.

What if a president could win an election by ‘guessing’ what would sway your vote?

President Obama engaged 50 analysts to assist him win the 2012 election using uplift modeling and predictive analytics. How? His team employed statistical algorithms to forecast which voters would be the easiest to influence and which media would persuade them the most.

In an interview, Rayid Ghani, the campaign’s chief data scientist for Obama for America 2012, mentioned:

Our modeling team created persuasion models for each swing state in order to forecast how persuasive each of the millions of residents would be. It advised us as to which people to avoid contacting at all costs and which were most likely to switch over to Obama’s side.

Obama’s team, in contrast to other political advertising campaigns, didn’t make distinctions based on psychographics and demographics; they accepted that not all middle-aged Latina women share the same opinions. Using predictive analytics, they made distinctions door by door and individual by person.

How does predictive analytics work for marketers?

The idiom “The whole is larger than the sum of its parts” is attributed to Aristotle. When it comes to the merging of datasets, this is typically true.

Say you work for Starbucks. Yes, the data from your CRM system and the frequency with which customers return to buy coffee are both valuable. If you combine those two datasets, you can build a personalized system that allows your staff to greet Marcus and inform him that he hasn’t purchased a coffee in five weeks despite doing it every day for two weeks in a row. A free venti cappuccino is available.

Once consumers get over the creep factor, marketers will profit from predictive analytics’ knowledge by offering increasingly individualized experiences that enhance customer satisfaction and loyalty. On top of that, there’s the age-old catch-22 of “I don’t know which half of my advertising spend is squandered.”

With the help of predictive analytics, you won’t just be aware of the most valuable channels; you’ll also be able to pinpoint the channels and people that make up the most value combination. It adds a completely new degree of segmentation, allowing marketers to better manage their money and increase their returns.

When will predictive analytics grow up?

Right now, predictive analytics is still relatively young. Only the most forward-thinking businesses are experimenting with it, and because predictive analytics is so specialized, there are currently relatively few accessible software platforms. Companies like Adobe and IBM are currently the leading service providers. However, I anticipate that it won’t be long until tech startups start to appear in this market and corporations like Google start to acquire them. In fact, Google has already released a prediction API for programmers to tinker with, indicating that they are interested in this field.

The future of algorithmic future gazing

At the nexus of personalization and prediction, one of the most exciting technological advancements about the future we’re moving into, can be found. There is space for increased efficiency and effectiveness through automation and optimization wherever data can be gathered and human behaviors predicted.

We now gather data on a scale that was never before possible. And we’re measuring things that weren’t measurable before. Take Jawbone Up as an example; as a result of millions of individuals downloading an app or donning a data-gathering wristband, they are undertaking the world’s largest study on coffee consumption and sleep.

Predictive analytics is a technology that, like many others, has both good and poor uses. There are many faulty systems that, in the appropriate hands, may be repaired or enhanced with greater prediction.

How do you feel? Too spooky or a fantastic opportunity? or a mix of the two?

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